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Abstract #1428

Quantitative Evaluation of Temporal Sparse Regularizers for Compressed Sensing Breast DCE-MRI

Dong Wang1, Lori R Arlinghaus2, Thomas E Yankeelov3, and David S Smith2

1School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, People's Republic of China, 2Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, 3Institute for Computational and Engineering Sciences, and the Departments of Biomedical Engineering and Internal Medicine, The University of Texas at Austin, Austin, Texas, USA

We quantitatively evaluate temporal sparse regularizers for breast DCE-MRI data under standard compressed sensing schemes. We consider five temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data, namely Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second order total generalized variation (TGV$$$_{\alpha}^{2}$$$) and nuclear norm (NN). Both signal-to-error ratio and concordance correlation coefficients of the derived pharmacokinetic parameters $$$K^{\text{trans}}$$$ (volume transfer constant) and $$$v_\mathrm{e}$$$ (extravascular extracellular volume fraction) are estimated. Results show that NN produces the lowest image error while TV/TGV$$$_{\alpha}^{2}$$$ produce the most accurate pharmacokinetic parameters.

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